Zheng Yao^*, Puqing Chang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068248 - 10 November 2025

Abstract As Internet of Things (IoT) applications expand, Mobile Edge Computing (MEC) has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices. Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies, conflicting objectives, and limited resources. This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC. We jointly consider task heterogeneity, high-dimensional objectives, and flexible resource scheduling, modeling the problem as a Many-objective optimization. To solve it, we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on More >

Fangzhen Ge^1,3, Yating Wu^1,*, Debao Chen^2,4, Longfeng Shen^1,5

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 189-211, 2024, DOI:10.32604/iasc.2024.042841 - 21 May 2024

Abstract It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is More >

Mengkai Zhao¹, Zhixia Zhang², Tian Fan¹, Wanwan Guo¹, Zhihua Cui^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2425-2450, 2023, DOI:10.32604/cmes.2023.026671 - 09 March 2023

Abstract Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based More >